In literature, various different approaches have been presented to incorporate the contextual information for the classification of histology images. Some researchers [1]}, [2]}, [3]} used image down-sampling, a common practice followed in natural image classification, to capture the context from larger histology image. However, this approach is not suitable for problems where cell information is as important as the context. Adaptive patch sampling [4]} and discriminative patch selection [5]} from histology images is another way to integrate the sparse context. These methods are not capable of capturing small regions of interest at high resolution e.g. tumor cells and their local contextual arrangement. Some methods [6]}, [7]}, [8]}, [9]} leverage the multi-resolution nature of histology images and use multi-resolution based classifiers to capture context. These multi-resolution approaches only consider a small part of an image at high resolution and the remaining part at low resolutions to make a prediction. Therefore, these approaches lack the contextual information of cellular architecture at high resolution in a histology image.
